I combined German designations under German and Russian designations under Russia. All other countries which have changed names over time, were left as is.
## # A tibble: 20 × 2
## region Year_Count
## <chr> <int>
## 1 Austria 22
## 2 Canada 22
## 3 Czech Republic 22
## 4 Finland 22
## 5 France 22
## 6 Hungary 22
## 7 Italy 22
## 8 Norway 22
## 9 Poland 22
## 10 Sweden 22
## 11 Switzerland 22
## 12 UK 22
## 13 USA 22
## 14 Belgium 20
## 15 Germany 20
## 16 Japan 20
## 17 Netherlands 20
## 18 Romania 20
## 19 Australia 19
## 20 Bulgaria 19
## # A tibble: 206 × 3
## # Groups: Country [10]
## Country Year Medal_Count
## <chr> <int> <int>
## 1 United States 2010 37
## 2 Germany 2002 36
## 3 United States 2002 34
## 4 Germany 1988 33
## 5 Russia 2014 33
## 6 Germany 2010 30
## 7 Germany 1976 29
## 8 Germany 1998 29
## 9 Germany 2006 29
## 10 Russia 1988 29
## # ℹ 196 more rows
Initially I made a line graph with the top 10 countries and a second
where they are separated in facets. I prefer the faceted version because
the initial line graph could be much clearer, and the facets make
interpretation a little easier.
The graph shows the top 10 countries with the largest difference
between any ranks. Some of the big winners (US, Russia, and China) all
fall in the rankings when they are adjusted for population. This
visualization is a little convoluted.
The graph shows that Russia, United States, China, Italy, etc (the Countries in Blue) ranked better after adjusting for GDP. The countries in purple ranked worse after adjusting for GDP.
The graph shows that Olympic winners like Russia, United States,
Germany, China, Canada, etc (all Countries in purple) performed much
worse after adjusting for population. The countries in blue performed
better after adjusting for Population.
Of the hosting countries, 6 have a higher average medals per Olympics
hosted, than not hosted. Some countries appear to have more of an
advantage when hosting. Conversely, only 3 seem to have a hosting
disadvantage: Austria, Switzerland, and Germany.
The graph shows the total medals earned by the 20 most successful winter athletes by the total numbers of medals won.
## # A tibble: 20 × 4
## # Groups: ID, Name [20]
## ID Name Sex Medal_Count
## <int> <chr> <chr> <int>
## 1 11951 "Ole Einar Bjrndalen" M 13
## 2 9747 "Stefania Belmondo" F 10
## 3 11943 "Marit Bjrgen" F 10
## 4 112111 "Raisa Petrovna Smetanina" F 10
## 5 28751 "Ursula \"Uschi\" Disl" F 9
## 6 54647 "Edy Sixten Jernberg" M 9
## 7 92566 "Claudia Pechstein" F 9
## 8 132791 "Lyubov Ivanovna Yegorova" F 9
## 9 20 "Kjetil Andr Aamodt" M 8
## 10 3604 "Viktor An" M 8
## 11 32700 "Karin Enke-Kania (-Busch-, -Richter)" F 8
## 12 35539 "Sven Fischer" M 8
## 13 43154 "Ricco Gro" M 8
## 14 64799 "Galina Alekseyevna Kulakova" F 8
## 15 86067 "Gunda Niemann-Stirnemann-Kleemann" F 8
## 16 88298 "Apolo Anton Ohno" M 8
## 17 131897 "Irene Karlijn \"Ireen\" Wst" F 8
## 18 7304 "Ivar Eugen Ballangrud (Eriksen-)" M 7
## 19 28063 "Manuela Di Centa" F 7
## 20 31659 "Andrea Ehrig-Schne-Mitscherlich" F 7
The graph shows the average height and weight of Olympic athletes who
won medals, grouped by Sex.
This initial graph is much improved upon when made interactive. A user can now more interpret the informatiopn by hovering over data points and clicking on the countries to tell which is which.
This interactive version allows the user to hover on the point and see the exact statistics as well as the exact sport the point belongs to without overcrowding the plot with labels.